ArticleBiometrics2020
Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data.
Article in Biometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Bayesian Transfer Learning.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.Statistics in medicine · 2025Article
- Article
- The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer.Frontiers in oral health · 2023Review
- Role of multiresolution vulnerability indices in COVID-19 spread in India: a Bayesian model-based analysis.BMJ open · 2022Observational
- Variational Bayes for high-dimensional proportional hazards models with applications within gene expression.Bioinformatics (Oxford, England) · 2022Article
- A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.BMC bioinformatics · 2022Article
- Challenges and Opportunities for Bayesian Statistics in Proteomics.Journal of proteome research · 2022Review
- Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2021Article
- Bayesian Modeling Immune Reconstitution Apply to CD34+ Selected Stem Cell Transplantation for Severe Combined Immunodeficiency.Frontiers in pediatrics · 2021Article
- A Pan-Cancer and Polygenic Bayesian Hierarchical Model for the Effect of Somatic Mutations on Survival.Cancer informatics · 2020Article
- The Application of Bayesian Methods in Cancer Prognosis and Prediction.Cancer genomics & proteomicsReview
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Authors and funding
4 authors.
Funding
Abstract
Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated "The Cancer Proteome Atlas" (TCPA), which contains reverse-phase protein arrays-based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.